All experiments are conducted on 8 NVIDIA Tesla V100 GPUs. Training mini-batch size is set as 48 and Adam [1]} is adopted for model parameter tuning.
Details of HASM. Our HASM is trained for 200k iterations with a learning rate of \(2.5 \times 10^{-3}\)  following the training setting of SegFormer [2]}.
Details of HDRM. Our HDRM has an input resolution of \(256 \times 256\) . And the loss weights are set as \(\lambda _1=0.1\) , \(\lambda _2=3.0\) , \(\lambda _3=0.1\) , \(\lambda _4=250.0\) . We first train HDRM with ground-truth masks for 100k iteration, and then fine-tune it with segmentation masks for another 100k iteration. The learning rate of these two stages are \(1.5 \times 10^{-3}\)  and \(1 \times 10^{-3}\)  respectively.
Details of SHPE. Our SHPE has an input resolution of \(256 \times 256\) . We train the network for 300k iterations with an initial learning rate of \(1 \times 10^{-3}\) . The learning rate is decayed to \(1 \times 10^{-4}\)  and \(1 \times 10^{-5}\)  at the 100k and 200k iterations respectively. Other training settings are kept the same as those of MinimalHand [3]}.
